IOSG: Pouring cold water on the prediction market

MarsBitNews

2025-12-01 15:36:47

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Author: Jiawei, IOSG

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Prediction Markets are undoubtedly one of the most prominent sectors in the cryptocurrency industry. The leading project Polymarket has an accumulated trading volume of over 36 billion dollars and recently completed a strategic round of financing with a valuation of 9 billion dollars. At the same time, platforms including Kalshi (valued at 11 billion dollars) have also received significant capital injections.

▲ Source: Dune

However, behind the continuous influx of capital and impressive data growth, we find that prediction markets, as a type of trading product, still face many challenges.

In this article, the author attempts to set aside mainstream optimism and provide some observations from a different perspective.

Predictions are based on events—events are essentially discontinuous and non-reproducible. Unlike the prices of assets such as stocks and foreign exchange, which change over time, prediction markets rely on a limited and discrete set of events in the real world. Compared to trading, it is low-frequency.

In the real world, there are very few events that have widespread attention, clear outcomes, and are settled within a reasonable time frame—presidential elections occur once every four years, the World Cup every four years, the Oscars once a year, and so on.

Most social, political, economic, and technological events do not have sustained trading demand. The number of such events is limited each year and their frequency is too low, making it difficult to build a stable trading ecosystem.

In other words, the low frequency of prediction markets is not something that can be easily changed by product design or incentive mechanisms. This underlying characteristic determines that the trading volume of prediction markets will inevitably not remain at a high level in the absence of significant events.

Prediction markets do not have fundamentals like stock markets: the value of the stock market comes from the intrinsic value of the company, including its future cash flows, profitability, assets, etc. Prediction markets ultimately point to an outcome, relying on users' “interest in the outcome of the event itself.”

(Of course, here we discuss the original intention of the product, excluding objective arbitrage and speculation factors, etc.; even in the stock market, there are many speculators who may not necessarily care about the essence of the underlying assets.)

In this context, the amount people are willing to bet is significantly positively correlated with the importance of the event, the market's attention, and the time frame: scarce and highly followed events such as the finals and presidential elections attract large amounts of capital and attention.

Naturally, an average fan is more likely to care about the outcome of the annual finals and place heavy bets on it, rather than showing such behavior during the regular season.

On Polymarket, the 2024 presidential election event accounts for more than 70% of the platform's total OI. At the same time, the vast majority of events remain in a state of low liquidity and high bid-ask spreads for a long time. From this perspective, it is difficult for the scale of the prediction market to expand exponentially.

Prediction markets have a gambling nature, but it is difficult to achieve the retention and expansion seen in gambling.

We all know that the real mechanism of gambling addiction lies in instant feedback—slot machines every few seconds, Texas Hold'em every few minutes, and contract and memecoin trading changing rapidly every second.

The feedback cycle of prediction markets is quite long, with most events taking weeks to months to settle. If it's a fast-feedback event, it may not be interesting enough to warrant a heavy bet.

Immediate feedback significantly increases the frequency of dopamine release, reinforcing user habits. Delayed feedback cannot establish stable user retention.

In some types of events, there is a high degree of information asymmetry among participants.

For competitive sports events, in addition to the paper strength between teams, a significant degree also relies on the athletes' performance on the spot, thus there is considerable uncertainty.

However, for political events, it involves internal information, channels, connections, and other black box processes. Insiders have a significant information advantage, making their bets much more certain.

Just like the vote counting process in elections, internal polls, and the organization in key areas, it is difficult for external participants to obtain this information. Currently, there has not yet been a clear definition from regulatory bodies regarding “insider trading” in prediction markets; this part remains a gray area.

Overall, for such events, the party at an informational disadvantage can easily become the exit liquidity.

Due to the ambiguity of language and definitions, it is also very difficult to be completely objective about the events in prediction markets.

For example: “Whether Russia and Ukraine will cease fire in 2025” depends on which statistical criterion is used; “Whether the cryptocurrency ETF is approved at a certain point in time,” which may include complete approval, partial approval, or conditional approval, and so on. This involves the issue of “social consensus”—when two sides are evenly matched, the losing side will not readily admit defeat.

Such ambiguity requires the platform to establish a dispute resolution mechanism. Once a prediction market encounters linguistic ambiguity and dispute resolution, it cannot fully rely on automation or objectivity, allowing for the possibility of human manipulation and corruption.

The main value proposition of prediction markets in the market is “collective intelligence,” which means that, with lower trust in media and mainstream discourse, prediction markets can aggregate the highest quality information globally, thereby achieving collective consensus.

However, before prediction markets achieve widespread adoption, this “information sampling” is bound to be one-sided, and the samples are not diverse enough. The user base of prediction market platforms may be highly homogeneous.

For example, in the early stages of prediction markets, it is definitely a platform primarily composed of cryptocurrency users whose views on political, social, and economic events may be highly convergent, thus forming an information bubble.

In this case, the market reflects the collective bias of a specific circle, and there is still quite a distance to go before reaching “collective intelligence.”

Conclusion

The core of this article is not to bear a bearish prediction on the market, but to hope that we can stay calm in the face of heightened FOMO emotions, especially after experiencing the ups and downs of popular narratives such as ZK and GameFi.

Over-reliance on special events like elections, the short-term sentiments of social media, and airdrop incentives often exaggerates the superficial data, which is still insufficient to support judgments about long-term growth.

Nonetheless, from the perspective of user education and user acquisition, prediction markets still hold an important position in the next three to five years. Similar to on-chain yield savings products, they have an intuitive product format and lower learning costs, making them more likely to attract users from outside the crypto ecosystem compared to on-chain trading protocols. Based on this, prediction markets are likely to further develop and, to some extent, become gateway products in the crypto industry.

Future prediction markets may also occupy certain verticals, such as sports and politics. They will continue to exist and expand, but in the short term, they do not have the foundational conditions for exponential growth. We should think about investing in prediction markets with a cautiously optimistic perspective.